 AWS LAMBDA TutorialAbout the Tutorial AWS Lambda is a service which computes the code without any server. It is said to be serverless compute. The code is executed based on the response of events in AWS services such 52 Authoring Lambda Code .......................................................................................................................... 52 Deploying Lambda Code ...................... ............................................................................. 53 Testing Lambda Code .................................................................................................0 码力 | 393 页 | 13.45 MB | 1 年前3 AWS LAMBDA TutorialAbout the Tutorial AWS Lambda is a service which computes the code without any server. It is said to be serverless compute. The code is executed based on the response of events in AWS services such 52 Authoring Lambda Code .......................................................................................................................... 52 Deploying Lambda Code ...................... ............................................................................. 53 Testing Lambda Code .................................................................................................0 码力 | 393 页 | 13.45 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.4.2enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2784 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2785 4.1.4 Contributing . . . . . . . . . 2812 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2813 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2820 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2821 ix 4.4.7 Running the test0 码力 | 3739 页 | 15.24 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.4.2enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2784 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2785 4.1.4 Contributing . . . . . . . . . 2812 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2813 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2820 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2821 ix 4.4.7 Running the test0 码力 | 3739 页 | 15.24 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.4.4enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2786 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2787 4.1.4 Contributing . . . . . . . . . 2814 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2815 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2822 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2823 ix 4.4.7 Running the test0 码力 | 3743 页 | 15.26 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.4.4enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2786 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2787 4.1.4 Contributing . . . . . . . . . 2814 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2815 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2822 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2823 ix 4.4.7 Running the test0 码力 | 3743 页 | 15.26 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.3.3enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2692 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing . . . . . . . . . 2720 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2721 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 ix 4.4.7 Running the test0 码力 | 3603 页 | 14.65 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.3.3enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2692 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing . . . . . . . . . 2720 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2721 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 ix 4.4.7 Running the test0 码力 | 3603 页 | 14.65 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.3.4enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2692 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing . . . . . . . . . 2720 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2721 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 ix 4.4.7 Running the test0 码力 | 3605 页 | 14.68 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.3.4enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2692 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2693 4.1.4 Contributing . . . . . . . . . 2720 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2721 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2728 ix 4.4.7 Running the test0 码力 | 3605 页 | 14.68 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.3.2enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 4.1.4 Contributing . . . . . . . . . 2641 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2642 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2648 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2649 ix 4.4.7 Running the test0 码力 | 3509 页 | 14.01 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.3.2enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 4.1.4 Contributing . . . . . . . . . 2641 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2642 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2648 4.4.6 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2649 ix 4.4.7 Running the test0 码力 | 3509 页 | 14.01 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 1.5.0rc0enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2956 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2957 4.1.4 Contributing . . . . . . . . . 2984 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2985 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2998 4.4.10 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2999 4.5 pandas maintenance0 码力 | 3943 页 | 15.73 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 1.5.0rc0enhancement requests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2956 4.1.3 Working with the code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2957 4.1.4 Contributing . . . . . . . . . 2984 4.4 Contributing to the code base . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2985 4.4.1 Code standards . . . . . . . . . . . . . . . . . . . suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2998 4.4.10 Documenting your code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2999 4.5 pandas maintenance0 码力 | 3943 页 | 15.73 MB | 1 年前3
 PyTorch Brand GuidelinesDon'ts Leverage the color palettes and keep things simple, ensuring there is a strong contrast between the symbol and the background. Don’t use colors that aren’t in the approved color palette or or primary brand color, please use it sparingly. We prefer to apply PyTorch Orange as a deliberate accent. To achieve the best AA compliance color contrast, PyTorch has a special color palette to best applying color in the digital environment; web, app, and social media posts, please reference the digital RGB or hex code equivalent. When printing, please use CMYK or the listed Pantone code. For0 码力 | 12 页 | 34.16 MB | 1 年前3 PyTorch Brand GuidelinesDon'ts Leverage the color palettes and keep things simple, ensuring there is a strong contrast between the symbol and the background. Don’t use colors that aren’t in the approved color palette or or primary brand color, please use it sparingly. We prefer to apply PyTorch Orange as a deliberate accent. To achieve the best AA compliance color contrast, PyTorch has a special color palette to best applying color in the digital environment; web, app, and social media posts, please reference the digital RGB or hex code equivalent. When printing, please use CMYK or the listed Pantone code. For0 码力 | 12 页 | 34.16 MB | 1 年前3
 Apache Kyuubi 1.3.0 Documentationtime: 2020-11-02T12:44:57.398Z User: kentyao 2020-11-02 20:51:49.501 INFO codegen.CodeGenerator: Code generated in 13.673142 ms 2020-11-02 20:51:49.625 INFO spark.SparkContext: Starting job: collect at ˓→ExecuteStatement.scala:49, took 0.503838 s 2020-11-02 20:51:50.151 INFO codegen.CodeGenerator: Code generated in 9.685752 ms 2020-11-02 20:51:50.228 INFO operation.ExecuteStatement: Processing kentyao's (i_color = 'powder' OR i_color = 'khaki') AND (i_units = 'Ounce' OR i_units = 'Oz') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'brown' OR i_color = 'honeydew')0 码力 | 129 页 | 6.15 MB | 1 年前3 Apache Kyuubi 1.3.0 Documentationtime: 2020-11-02T12:44:57.398Z User: kentyao 2020-11-02 20:51:49.501 INFO codegen.CodeGenerator: Code generated in 13.673142 ms 2020-11-02 20:51:49.625 INFO spark.SparkContext: Starting job: collect at ˓→ExecuteStatement.scala:49, took 0.503838 s 2020-11-02 20:51:50.151 INFO codegen.CodeGenerator: Code generated in 9.685752 ms 2020-11-02 20:51:50.228 INFO operation.ExecuteStatement: Processing kentyao's (i_color = 'powder' OR i_color = 'khaki') AND (i_units = 'Ounce' OR i_units = 'Oz') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'brown' OR i_color = 'honeydew')0 码力 | 129 页 | 6.15 MB | 1 年前3
 Apache Kyuubi 1.3.1 Documentationtime: 2020-11-02T12:44:57.398Z User: kentyao 2020-11-02 20:51:49.501 INFO codegen.CodeGenerator: Code generated in 13.673142 ms 2020-11-02 20:51:49.625 INFO spark.SparkContext: Starting job: collect at ˓→ExecuteStatement.scala:49, took 0.503838 s 2020-11-02 20:51:50.151 INFO codegen.CodeGenerator: Code generated in 9.685752 ms 2020-11-02 20:51:50.228 INFO operation.ExecuteStatement: Processing kentyao's (i_color = 'powder' OR i_color = 'khaki') AND (i_units = 'Ounce' OR i_units = 'Oz') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'brown' OR i_color = 'honeydew')0 码力 | 129 页 | 6.16 MB | 1 年前3 Apache Kyuubi 1.3.1 Documentationtime: 2020-11-02T12:44:57.398Z User: kentyao 2020-11-02 20:51:49.501 INFO codegen.CodeGenerator: Code generated in 13.673142 ms 2020-11-02 20:51:49.625 INFO spark.SparkContext: Starting job: collect at ˓→ExecuteStatement.scala:49, took 0.503838 s 2020-11-02 20:51:50.151 INFO codegen.CodeGenerator: Code generated in 9.685752 ms 2020-11-02 20:51:50.228 INFO operation.ExecuteStatement: Processing kentyao's (i_color = 'powder' OR i_color = 'khaki') AND (i_units = 'Ounce' OR i_units = 'Oz') AND (i_size = 'medium' OR i_size = 'extra large') ) OR (i_category = 'Women' AND (i_color = 'brown' OR i_color = 'honeydew')0 码力 | 129 页 | 6.16 MB | 1 年前3
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